from functools import partial import numpy as np from nnlib import nnlib from models import ModelBase from facelib import FaceType from samplelib import * from interact import interact as io #SAE - Styled AutoEncoder class SAEModel(ModelBase): encoderH5 = 'encoder.h5' inter_BH5 = 'inter_B.h5' inter_ABH5 = 'inter_AB.h5' decoderH5 = 'decoder.h5' decodermH5 = 'decoderm.h5' decoder_srcH5 = 'decoder_src.h5' decoder_srcmH5 = 'decoder_srcm.h5' decoder_dstH5 = 'decoder_dst.h5' decoder_dstmH5 = 'decoder_dstm.h5' #override def onInitializeOptions(self, is_first_run, ask_override): yn_str = {True:'y',False:'n'} default_resolution = 128 default_archi = 'df' default_face_type = 'f' if is_first_run: resolution = io.input_int("Resolution ( 64-256 ?:help skip:128) : ", default_resolution, help_message="More resolution requires more VRAM and time to train. Value will be adjusted to multiple of 16.") resolution = np.clip (resolution, 64, 256) while np.modf(resolution / 16)[0] != 0.0: resolution -= 1 self.options['resolution'] = resolution self.options['face_type'] = io.input_str ("Half or Full face? (h/f, ?:help skip:f) : ", default_face_type, ['h','f'], help_message="Half face has better resolution, but covers less area of cheeks.").lower() self.options['learn_mask'] = io.input_bool ("Learn mask? (y/n, ?:help skip:y) : ", True, help_message="Learning mask can help model to recognize face directions. Learn without mask can reduce model size, in this case converter forced to use 'not predicted mask' that is not smooth as predicted. Model with style values can be learned without mask and produce same quality result.") else: self.options['resolution'] = self.options.get('resolution', default_resolution) self.options['face_type'] = self.options.get('face_type', default_face_type) self.options['learn_mask'] = self.options.get('learn_mask', True) if (is_first_run or ask_override) and 'tensorflow' in self.device_config.backend: def_optimizer_mode = self.options.get('optimizer_mode', 1) self.options['optimizer_mode'] = io.input_int ("Optimizer mode? ( 1,2,3 ?:help skip:%d) : " % (def_optimizer_mode), def_optimizer_mode, help_message="1 - no changes. 2 - allows you to train x2 bigger network consuming RAM. 3 - allows you to train x3 bigger network consuming huge amount of RAM and slower, depends on CPU power.") else: self.options['optimizer_mode'] = self.options.get('optimizer_mode', 1) if is_first_run: self.options['archi'] = io.input_str ("AE architecture (df, liae ?:help skip:%s) : " % (default_archi) , default_archi, ['df','liae'], help_message="'df' keeps faces more natural. 'liae' can fix overly different face shapes.").lower() #-s version is slower, but has decreased change to collapse. else: self.options['archi'] = self.options.get('archi', default_archi) default_ae_dims = 256 if 'liae' in self.options['archi'] else 512 default_e_ch_dims = 42 default_d_ch_dims = default_e_ch_dims // 2 def_ca_weights = False if is_first_run: self.options['ae_dims'] = np.clip ( io.input_int("AutoEncoder dims (32-1024 ?:help skip:%d) : " % (default_ae_dims) , default_ae_dims, help_message="All face information will packed to AE dims. If amount of AE dims are not enough, then for example closed eyes will not be recognized. More dims are better, but require more VRAM. You can fine-tune model size to fit your GPU." ), 32, 1024 ) self.options['e_ch_dims'] = np.clip ( io.input_int("Encoder dims per channel (21-85 ?:help skip:%d) : " % (default_e_ch_dims) , default_e_ch_dims, help_message="More encoder dims help to recognize more facial features, but require more VRAM. You can fine-tune model size to fit your GPU." ), 21, 85 ) default_d_ch_dims = self.options['e_ch_dims'] // 2 self.options['d_ch_dims'] = np.clip ( io.input_int("Decoder dims per channel (10-85 ?:help skip:%d) : " % (default_d_ch_dims) , default_d_ch_dims, help_message="More decoder dims help to get better details, but require more VRAM. You can fine-tune model size to fit your GPU." ), 10, 85 ) #self.options['remove_gray_border'] = io.input_bool ("Remove gray border? (y/n, ?:help skip:n) : ", False, help_message="Removes gray border of predicted face, but requires more computing resources.") self.options['multiscale_decoder'] = io.input_bool ("Use multiscale decoder? (y/n, ?:help skip:n) : ", False, help_message="Multiscale decoder helps to get better details.") self.options['ca_weights'] = io.input_bool ("Use CA weights? (y/n, ?:help skip: %s ) : " % (yn_str[def_ca_weights]), def_ca_weights, help_message="Initialize network with 'Convolution Aware' weights. This may help to achieve a higher accuracy model, but consumes a time at first run.") else: self.options['ae_dims'] = self.options.get('ae_dims', default_ae_dims) self.options['e_ch_dims'] = self.options.get('e_ch_dims', default_e_ch_dims) self.options['d_ch_dims'] = self.options.get('d_ch_dims', default_d_ch_dims) self.options['remove_gray_border'] = self.options.get('remove_gray_border', False) self.options['multiscale_decoder'] = self.options.get('multiscale_decoder', False) self.options['ca_weights'] = self.options.get('ca_weights', def_ca_weights) default_face_style_power = 0.0 default_bg_style_power = 0.0 if is_first_run or ask_override: def_pixel_loss = self.options.get('pixel_loss', False) self.options['pixel_loss'] = io.input_bool ("Use pixel loss? (y/n, ?:help skip: %s ) : " % (yn_str[def_pixel_loss]), def_pixel_loss, help_message="Pixel loss may help to enhance fine details and stabilize face color. Use it only if quality does not improve over time.") default_face_style_power = default_face_style_power if is_first_run else self.options.get('face_style_power', default_face_style_power) self.options['face_style_power'] = np.clip ( io.input_number("Face style power ( 0.0 .. 100.0 ?:help skip:%.2f) : " % (default_face_style_power), default_face_style_power, help_message="Learn to transfer face style details such as light and color conditions. Warning: Enable it only after 10k iters, when predicted face is clear enough to start learn style. Start from 0.1 value and check history changes."), 0.0, 100.0 ) default_bg_style_power = default_bg_style_power if is_first_run else self.options.get('bg_style_power', default_bg_style_power) self.options['bg_style_power'] = np.clip ( io.input_number("Background style power ( 0.0 .. 100.0 ?:help skip:%.2f) : " % (default_bg_style_power), default_bg_style_power, help_message="Learn to transfer image around face. This can make face more like dst."), 0.0, 100.0 ) else: self.options['pixel_loss'] = self.options.get('pixel_loss', False) self.options['face_style_power'] = self.options.get('face_style_power', default_face_style_power) self.options['bg_style_power'] = self.options.get('bg_style_power', default_bg_style_power) #override def onInitialize(self): exec(nnlib.import_all(), locals(), globals()) SAEModel.initialize_nn_functions() self.set_vram_batch_requirements({1.5:4}) resolution = self.options['resolution'] ae_dims = self.options['ae_dims'] e_ch_dims = self.options['e_ch_dims'] d_ch_dims = self.options['d_ch_dims'] d_residual_blocks = True bgr_shape = (resolution, resolution, 3) mask_shape = (resolution, resolution, 1) self.ms_count = ms_count = 3 if (self.options['multiscale_decoder']) else 1 masked_training = True warped_src = Input(bgr_shape) target_src = Input(bgr_shape) target_srcm = Input(mask_shape) warped_dst = Input(bgr_shape) target_dst = Input(bgr_shape) target_dstm = Input(mask_shape) target_src_ar = [ Input ( ( bgr_shape[0] // (2**i) ,)*2 + (bgr_shape[-1],) ) for i in range(ms_count-1, -1, -1)] target_srcm_ar = [ Input ( ( mask_shape[0] // (2**i) ,)*2 + (mask_shape[-1],) ) for i in range(ms_count-1, -1, -1)] target_dst_ar = [ Input ( ( bgr_shape[0] // (2**i) ,)*2 + (bgr_shape[-1],) ) for i in range(ms_count-1, -1, -1)] target_dstm_ar = [ Input ( ( mask_shape[0] // (2**i) ,)*2 + (mask_shape[-1],) ) for i in range(ms_count-1, -1, -1)] padding = 'zero' norm = '' if '-s' in self.options['archi']: norm = 'bn' common_flow_kwargs = { 'padding': padding, 'norm': norm, 'act':'' } models_list = [] weights_to_load = [] if 'liae' in self.options['archi']: self.encoder = modelify(SAEModel.LIAEEncFlow(resolution, ch_dims=e_ch_dims, **common_flow_kwargs) ) (Input(bgr_shape)) enc_output_Inputs = [ Input(K.int_shape(x)[1:]) for x in self.encoder.outputs ] self.inter_B = modelify(SAEModel.LIAEInterFlow(resolution, ae_dims=ae_dims, **common_flow_kwargs)) (enc_output_Inputs) self.inter_AB = modelify(SAEModel.LIAEInterFlow(resolution, ae_dims=ae_dims, **common_flow_kwargs)) (enc_output_Inputs) inter_output_Inputs = [ Input( np.array(K.int_shape(x)[1:])*(1,1,2) ) for x in self.inter_B.outputs ] self.decoder = modelify(SAEModel.LIAEDecFlow (bgr_shape[2],ch_dims=d_ch_dims, multiscale_count=self.ms_count, add_residual_blocks=d_residual_blocks, **common_flow_kwargs)) (inter_output_Inputs) models_list += [self.encoder, self.inter_B, self.inter_AB, self.decoder] if self.options['learn_mask']: self.decoderm = modelify(SAEModel.LIAEDecFlow (mask_shape[2],ch_dims=d_ch_dims, **common_flow_kwargs)) (inter_output_Inputs) models_list += [self.decoderm] if not self.is_first_run(): weights_to_load += [ [self.encoder , 'encoder.h5'], [self.inter_B , 'inter_B.h5'], [self.inter_AB, 'inter_AB.h5'], [self.decoder , 'decoder.h5'], ] if self.options['learn_mask']: weights_to_load += [ [self.decoderm, 'decoderm.h5'] ] warped_src_code = self.encoder (warped_src) warped_src_inter_AB_code = self.inter_AB (warped_src_code) warped_src_inter_code = Concatenate()([warped_src_inter_AB_code,warped_src_inter_AB_code]) warped_dst_code = self.encoder (warped_dst) warped_dst_inter_B_code = self.inter_B (warped_dst_code) warped_dst_inter_AB_code = self.inter_AB (warped_dst_code) warped_dst_inter_code = Concatenate()([warped_dst_inter_B_code,warped_dst_inter_AB_code]) warped_src_dst_inter_code = Concatenate()([warped_dst_inter_AB_code,warped_dst_inter_AB_code]) pred_src_src = self.decoder(warped_src_inter_code) pred_dst_dst = self.decoder(warped_dst_inter_code) pred_src_dst = self.decoder(warped_src_dst_inter_code) if self.options['learn_mask']: pred_src_srcm = self.decoderm(warped_src_inter_code) pred_dst_dstm = self.decoderm(warped_dst_inter_code) pred_src_dstm = self.decoderm(warped_src_dst_inter_code) elif 'df' in self.options['archi']: self.encoder = modelify(SAEModel.DFEncFlow(resolution, ae_dims=ae_dims, ch_dims=e_ch_dims, **common_flow_kwargs) ) (Input(bgr_shape)) dec_Inputs = [ Input(K.int_shape(x)[1:]) for x in self.encoder.outputs ] self.decoder_src = modelify(SAEModel.DFDecFlow (bgr_shape[2],ch_dims=d_ch_dims, multiscale_count=self.ms_count, add_residual_blocks=d_residual_blocks, **common_flow_kwargs )) (dec_Inputs) self.decoder_dst = modelify(SAEModel.DFDecFlow (bgr_shape[2],ch_dims=d_ch_dims, multiscale_count=self.ms_count, add_residual_blocks=d_residual_blocks, **common_flow_kwargs )) (dec_Inputs) models_list += [self.encoder, self.decoder_src, self.decoder_dst] if self.options['learn_mask']: self.decoder_srcm = modelify(SAEModel.DFDecFlow (mask_shape[2],ch_dims=d_ch_dims, **common_flow_kwargs )) (dec_Inputs) self.decoder_dstm = modelify(SAEModel.DFDecFlow (mask_shape[2],ch_dims=d_ch_dims, **common_flow_kwargs )) (dec_Inputs) models_list += [self.decoder_srcm, self.decoder_dstm] if not self.is_first_run(): weights_to_load += [ [self.encoder , 'encoder.h5'], [self.decoder_src, 'decoder_src.h5'], [self.decoder_dst, 'decoder_dst.h5'] ] if self.options['learn_mask']: weights_to_load += [ [self.decoder_srcm, 'decoder_srcm.h5'], [self.decoder_dstm, 'decoder_dstm.h5'], ] warped_src_code = self.encoder (warped_src) warped_dst_code = self.encoder (warped_dst) pred_src_src = self.decoder_src(warped_src_code) pred_dst_dst = self.decoder_dst(warped_dst_code) pred_src_dst = self.decoder_src(warped_dst_code) if self.options['learn_mask']: pred_src_srcm = self.decoder_srcm(warped_src_code) pred_dst_dstm = self.decoder_dstm(warped_dst_code) pred_src_dstm = self.decoder_srcm(warped_dst_code) if self.is_first_run() and self.options.get('ca_weights',False): conv_weights_list = [] for model in models_list: for layer in model.layers: if type(layer) == keras.layers.Conv2D: conv_weights_list += [layer.weights[0]] #Conv2D kernel_weights CAInitializerMP ( conv_weights_list ) pred_src_src, pred_dst_dst, pred_src_dst, = [ [x] if type(x) != list else x for x in [pred_src_src, pred_dst_dst, pred_src_dst, ] ] if self.options['learn_mask']: pred_src_srcm, pred_dst_dstm, pred_src_dstm = [ [x] if type(x) != list else x for x in [pred_src_srcm, pred_dst_dstm, pred_src_dstm] ] target_srcm_blurred_ar = [ gaussian_blur( max(1, K.int_shape(x)[1] // 32) )(x) for x in target_srcm_ar] target_srcm_sigm_ar = target_srcm_blurred_ar #[ x / 2.0 + 0.5 for x in target_srcm_blurred_ar] target_srcm_anti_sigm_ar = [ 1.0 - x for x in target_srcm_sigm_ar] target_dstm_blurred_ar = [ gaussian_blur( max(1, K.int_shape(x)[1] // 32) )(x) for x in target_dstm_ar] target_dstm_sigm_ar = target_dstm_blurred_ar#[ x / 2.0 + 0.5 for x in target_dstm_blurred_ar] target_dstm_anti_sigm_ar = [ 1.0 - x for x in target_dstm_sigm_ar] target_src_sigm_ar = target_src_ar#[ x + 1 for x in target_src_ar] target_dst_sigm_ar = target_dst_ar#[ x + 1 for x in target_dst_ar] pred_src_src_sigm_ar = pred_src_src#[ x + 1 for x in pred_src_src] pred_dst_dst_sigm_ar = pred_dst_dst#[ x + 1 for x in pred_dst_dst] pred_src_dst_sigm_ar = pred_src_dst#[ x + 1 for x in pred_src_dst] target_src_masked_ar = [ target_src_sigm_ar[i]*target_srcm_sigm_ar[i] for i in range(len(target_src_sigm_ar))] target_dst_masked_ar = [ target_dst_sigm_ar[i]*target_dstm_sigm_ar[i] for i in range(len(target_dst_sigm_ar))] target_dst_anti_masked_ar = [ target_dst_sigm_ar[i]*target_dstm_anti_sigm_ar[i] for i in range(len(target_dst_sigm_ar))] pred_src_src_masked_ar = [ pred_src_src_sigm_ar[i] * target_srcm_sigm_ar[i] for i in range(len(pred_src_src_sigm_ar))] pred_dst_dst_masked_ar = [ pred_dst_dst_sigm_ar[i] * target_dstm_sigm_ar[i] for i in range(len(pred_dst_dst_sigm_ar))] target_src_masked_ar_opt = target_src_masked_ar if masked_training else target_src_sigm_ar target_dst_masked_ar_opt = target_dst_masked_ar if masked_training else target_dst_sigm_ar pred_src_src_masked_ar_opt = pred_src_src_masked_ar if masked_training else pred_src_src_sigm_ar pred_dst_dst_masked_ar_opt = pred_dst_dst_masked_ar if masked_training else pred_dst_dst_sigm_ar psd_target_dst_masked_ar = [ pred_src_dst_sigm_ar[i]*target_dstm_sigm_ar[i] for i in range(len(pred_src_dst_sigm_ar))] psd_target_dst_anti_masked_ar = [ pred_src_dst_sigm_ar[i]*target_dstm_anti_sigm_ar[i] for i in range(len(pred_src_dst_sigm_ar))] if self.is_training_mode: self.src_dst_opt = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999, tf_cpu_mode=self.options['optimizer_mode']-1) self.src_dst_mask_opt = Adam(lr=5e-5, beta_1=0.5, beta_2=0.999, tf_cpu_mode=self.options['optimizer_mode']-1) if 'liae' in self.options['archi']: src_dst_loss_train_weights = self.encoder.trainable_weights + self.inter_B.trainable_weights + self.inter_AB.trainable_weights + self.decoder.trainable_weights if self.options['learn_mask']: src_dst_mask_loss_train_weights = self.encoder.trainable_weights + self.inter_B.trainable_weights + self.inter_AB.trainable_weights + self.decoderm.trainable_weights else: src_dst_loss_train_weights = self.encoder.trainable_weights + self.decoder_src.trainable_weights + self.decoder_dst.trainable_weights if self.options['learn_mask']: src_dst_mask_loss_train_weights = self.encoder.trainable_weights + self.decoder_srcm.trainable_weights + self.decoder_dstm.trainable_weights if not self.options['pixel_loss']: src_loss_batch = sum([ 10*dssim(kernel_size=int(resolution/11.6),max_value=1.0)( target_src_masked_ar_opt[i], pred_src_src_masked_ar_opt[i]) for i in range(len(target_src_masked_ar_opt)) ]) else: src_loss_batch = sum([ K.mean ( 50*K.square( target_src_masked_ar_opt[i] - pred_src_src_masked_ar_opt[i] ), axis=[1,2,3]) for i in range(len(target_src_masked_ar_opt)) ]) src_loss = K.mean(src_loss_batch) face_style_power = self.options['face_style_power'] / 100.0 if face_style_power != 0: src_loss += style_loss(gaussian_blur_radius=resolution//16, loss_weight=face_style_power, wnd_size=0)( psd_target_dst_masked_ar[-1], target_dst_masked_ar[-1] ) bg_style_power = self.options['bg_style_power'] / 100.0 if bg_style_power != 0: if not self.options['pixel_loss']: bg_loss = K.mean( (10*bg_style_power)*dssim(kernel_size=int(resolution/11.6),max_value=1.0)( psd_target_dst_anti_masked_ar[-1], target_dst_anti_masked_ar[-1] )) else: bg_loss = K.mean( (50*bg_style_power)*K.square( psd_target_dst_anti_masked_ar[-1] - target_dst_anti_masked_ar[-1] )) src_loss += bg_loss if not self.options['pixel_loss']: dst_loss_batch = sum([ 10*dssim(kernel_size=int(resolution/11.6),max_value=1.0)(target_dst_masked_ar_opt[i], pred_dst_dst_masked_ar_opt[i]) for i in range(len(target_dst_masked_ar_opt)) ]) else: dst_loss_batch = sum([ K.mean ( 50*K.square( target_dst_masked_ar_opt[i] - pred_dst_dst_masked_ar_opt[i] ), axis=[1,2,3]) for i in range(len(target_dst_masked_ar_opt)) ]) dst_loss = K.mean(dst_loss_batch) feed = [warped_src, warped_dst] feed += target_src_ar[::-1] feed += target_srcm_ar[::-1] feed += target_dst_ar[::-1] feed += target_dstm_ar[::-1] self.src_dst_train = K.function (feed,[src_loss,dst_loss], self.src_dst_opt.get_updates(src_loss+dst_loss, src_dst_loss_train_weights) ) if self.options['learn_mask']: src_mask_loss = sum([ K.mean(K.square(target_srcm_ar[-1]-pred_src_srcm[-1])) for i in range(len(target_srcm_ar)) ]) dst_mask_loss = sum([ K.mean(K.square(target_dstm_ar[-1]-pred_dst_dstm[-1])) for i in range(len(target_dstm_ar)) ]) feed = [ warped_src, warped_dst] feed += target_srcm_ar[::-1] feed += target_dstm_ar[::-1] self.src_dst_mask_train = K.function (feed,[src_mask_loss, dst_mask_loss], self.src_dst_mask_opt.get_updates(src_mask_loss+dst_mask_loss, src_dst_mask_loss_train_weights) ) if self.options['learn_mask']: self.AE_view = K.function ([warped_src, warped_dst], [pred_src_src[-1], pred_dst_dst[-1], pred_dst_dstm[-1], pred_src_dst[-1], pred_src_dstm[-1]]) else: self.AE_view = K.function ([warped_src, warped_dst], [pred_src_src[-1], pred_dst_dst[-1], pred_src_dst[-1] ] ) self.load_weights_safe(weights_to_load)#, [ [self.src_dst_opt, 'src_dst_opt'], [self.src_dst_mask_opt, 'src_dst_mask_opt']]) else: self.load_weights_safe(weights_to_load) if self.options['learn_mask']: self.AE_convert = K.function ([warped_dst],[ pred_src_dst[-1], pred_dst_dstm[-1], pred_src_dstm[-1] ]) else: self.AE_convert = K.function ([warped_dst],[ pred_src_dst[-1] ]) if self.is_training_mode: self.src_sample_losses = [] self.dst_sample_losses = [] t = SampleProcessor.Types face_type = t.FACE_TYPE_FULL if self.options['face_type'] == 'f' else t.FACE_TYPE_HALF output_sample_types = [ {'types' : (t.IMG_WARPED_TRANSFORMED, face_type, t.MODE_BGR), 'resolution':resolution} ] output_sample_types += [ {'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_BGR), 'resolution': resolution // (2**i) } for i in range(ms_count)] output_sample_types += [ {'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_M, t.FACE_MASK_FULL), 'resolution': resolution // (2**i) } for i in range(ms_count)] self.set_training_data_generators ([ SampleGeneratorFace(self.training_data_src_path, sort_by_yaw_target_samples_path=self.training_data_dst_path if self.sort_by_yaw else None, debug=self.is_debug(), batch_size=self.batch_size, sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, scale_range=np.array([-0.05, 0.05])+self.src_scale_mod / 100.0 ), output_sample_types=output_sample_types ), SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size, sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, ), output_sample_types=output_sample_types ) ]) #override def onSave(self): opt_ar = [ [self.src_dst_opt, 'src_dst_opt'], [self.src_dst_mask_opt, 'src_dst_mask_opt'] ] ar = [] if 'liae' in self.options['archi']: ar += [[self.encoder, 'encoder.h5'], [self.inter_B, 'inter_B.h5'], [self.inter_AB, 'inter_AB.h5'], [self.decoder, 'decoder.h5'] ] if self.options['learn_mask']: ar += [ [self.decoderm, 'decoderm.h5'] ] elif 'df' in self.options['archi']: ar += [[self.encoder, 'encoder.h5'], [self.decoder_src, 'decoder_src.h5'], [self.decoder_dst, 'decoder_dst.h5'] ] if self.options['learn_mask']: ar += [ [self.decoder_srcm, 'decoder_srcm.h5'], [self.decoder_dstm, 'decoder_dstm.h5'] ] self.save_weights_safe(ar) #override def onTrainOneIter(self, generators_samples, generators_list): src_samples = generators_samples[0] dst_samples = generators_samples[1] feed = [src_samples[0], dst_samples[0] ] + \ src_samples[1:1+self.ms_count*2] + \ dst_samples[1:1+self.ms_count*2] src_loss, dst_loss, = self.src_dst_train (feed) if self.options['learn_mask']: feed = [ src_samples[0], dst_samples[0] ] + \ src_samples[1+self.ms_count:1+self.ms_count*2] + \ dst_samples[1+self.ms_count:1+self.ms_count*2] src_mask_loss, dst_mask_loss, = self.src_dst_mask_train (feed) return ( ('src_loss', src_loss), ('dst_loss', dst_loss) ) #override def onGetPreview(self, sample): test_S = sample[0][1][0:4] #first 4 samples test_S_m = sample[0][1+self.ms_count][0:4] #first 4 samples test_D = sample[1][1][0:4] test_D_m = sample[1][1+self.ms_count][0:4] if self.options['learn_mask']: S, D, SS, DD, DDM, SD, SDM = [ np.clip(x, 0.0, 1.0) for x in ([test_S,test_D] + self.AE_view ([test_S, test_D]) ) ] DDM, SDM, = [ np.repeat (x, (3,), -1) for x in [DDM, SDM] ] else: S, D, SS, DD, SD, = [ np.clip(x, 0.0, 1.0) for x in ([test_S,test_D] + self.AE_view ([test_S, test_D]) ) ] result = [] st = [] for i in range(0, len(test_S)): ar = S[i], SS[i], D[i], DD[i], SD[i] st.append ( np.concatenate ( ar, axis=1) ) result += [ ('SAE', np.concatenate (st, axis=0 )), ] if self.options['learn_mask']: st_m = [] for i in range(0, len(test_S)): ar = S[i]*test_S_m[i], SS[i], D[i]*test_D_m[i], DD[i]*DDM[i], SD[i]*(DDM[i]*SDM[i]) st_m.append ( np.concatenate ( ar, axis=1) ) result += [ ('SAE masked', np.concatenate (st_m, axis=0 )), ] return result def predictor_func (self, face): if self.options['learn_mask']: bgr, mask_dst_dstm, mask_src_dstm = self.AE_convert ([face[np.newaxis,...]]) mask = mask_dst_dstm[0] * mask_src_dstm[0] return bgr[0], mask[...,0] else: bgr, = self.AE_convert ([face[np.newaxis,...]]) return bgr[0] #override def get_converter(self): base_erode_mask_modifier = 30 if self.options['face_type'] == 'f' else 100 base_blur_mask_modifier = 0 if self.options['face_type'] == 'f' else 100 default_erode_mask_modifier = 0 default_blur_mask_modifier = 100 if (self.options['face_style_power'] or self.options['bg_style_power']) and \ self.options['face_type'] == 'f' else 0 face_type = FaceType.FULL if self.options['face_type'] == 'f' else FaceType.HALF from converters import ConverterMasked return ConverterMasked(self.predictor_func, predictor_input_size=self.options['resolution'], predictor_masked=self.options['learn_mask'], face_type=face_type, default_mode = 1 if self.options['face_style_power'] or self.options['bg_style_power'] else 4, base_erode_mask_modifier=base_erode_mask_modifier, base_blur_mask_modifier=base_blur_mask_modifier, default_erode_mask_modifier=default_erode_mask_modifier, default_blur_mask_modifier=default_blur_mask_modifier, clip_hborder_mask_per=0.0625 if (not self.options['remove_gray_border'] and self.options['face_type'] == 'f') else 0) @staticmethod def initialize_nn_functions(): exec (nnlib.import_all(), locals(), globals()) def NormPass(x): return x def Norm(norm=''): if norm == 'bn': return BatchNormalization(axis=-1) else: return NormPass def Act(act='', lrelu_alpha=0.1): if act == 'prelu': return PReLU() else: return LeakyReLU(alpha=lrelu_alpha) class ResidualBlock(object): def __init__(self, filters, kernel_size=3, padding='zero', norm='', act='', **kwargs): self.filters = filters self.kernel_size = kernel_size self.padding = padding self.norm = norm self.act = act def __call__(self, inp): x = inp x = Conv2D(self.filters, kernel_size=self.kernel_size, padding=self.padding)(x) x = Act(self.act, lrelu_alpha=0.2)(x) x = Norm(self.norm)(x) x = Conv2D(self.filters, kernel_size=self.kernel_size, padding=self.padding)(x) x = Add()([x, inp]) x = Act(self.act, lrelu_alpha=0.2)(x) x = Norm(self.norm)(x) return x SAEModel.ResidualBlock = ResidualBlock def downscale (dim, padding='zero', norm='', act='', **kwargs): def func(x): return Norm(norm)( Act(act) (Conv2D(dim, kernel_size=5, strides=2, padding=padding)(x)) ) return func SAEModel.downscale = downscale def upscale (dim, padding='zero', norm='', act='', **kwargs): def func(x): return SubpixelUpscaler()(Norm(norm)(Act(act)(Conv2D(dim * 4, kernel_size=3, strides=1, padding=padding)(x)))) return func SAEModel.upscale = upscale def to_bgr (output_nc, padding='zero', **kwargs): def func(x): return Conv2D(output_nc, kernel_size=5, padding=padding, activation='sigmoid')(x) return func SAEModel.to_bgr = to_bgr @staticmethod def LIAEEncFlow(resolution, ch_dims, **kwargs): exec (nnlib.import_all(), locals(), globals()) upscale = partial(SAEModel.upscale, **kwargs) downscale = partial(SAEModel.downscale, **kwargs) def func(input): dims = K.int_shape(input)[-1]*ch_dims x = input x = downscale(dims)(x) x = downscale(dims*2)(x) x = downscale(dims*4)(x) x = downscale(dims*8)(x) x = Flatten()(x) return x return func @staticmethod def LIAEInterFlow(resolution, ae_dims=256, **kwargs): exec (nnlib.import_all(), locals(), globals()) upscale = partial(SAEModel.upscale, **kwargs) lowest_dense_res=resolution // 16 def func(input): x = input[0] x = Dense(ae_dims)(x) x = Dense(lowest_dense_res * lowest_dense_res * ae_dims*2)(x) x = Reshape((lowest_dense_res, lowest_dense_res, ae_dims*2))(x) x = upscale(ae_dims*2)(x) return x return func @staticmethod def LIAEDecFlow(output_nc,ch_dims, multiscale_count=1, add_residual_blocks=False, **kwargs): exec (nnlib.import_all(), locals(), globals()) upscale = partial(SAEModel.upscale, **kwargs) to_bgr = partial(SAEModel.to_bgr, **kwargs) dims = output_nc * ch_dims ResidualBlock = partial(SAEModel.ResidualBlock, **kwargs) def func(input): x = input[0] outputs = [] x1 = upscale(dims*8)( x ) if add_residual_blocks: x1 = ResidualBlock(dims*8)(x1) x1 = ResidualBlock(dims*8)(x1) if multiscale_count >= 3: outputs += [ to_bgr(output_nc) ( x1 ) ] x2 = upscale(dims*4)( x1 ) if add_residual_blocks: x2 = ResidualBlock(dims*4)(x2) x2 = ResidualBlock(dims*4)(x2) if multiscale_count >= 2: outputs += [ to_bgr(output_nc) ( x2 ) ] x3 = upscale(dims*2)( x2 ) if add_residual_blocks: x3 = ResidualBlock( dims*2)(x3) x3 = ResidualBlock( dims*2)(x3) outputs += [ to_bgr(output_nc) ( x3 ) ] return outputs return func @staticmethod def DFEncFlow(resolution, ae_dims, ch_dims, **kwargs): exec (nnlib.import_all(), locals(), globals()) upscale = partial(SAEModel.upscale, **kwargs) downscale = partial(SAEModel.downscale, **kwargs)#, kernel_regularizer=keras.regularizers.l2(0.0), lowest_dense_res = resolution // 16 def func(input): x = input dims = K.int_shape(input)[-1]*ch_dims x = downscale(dims)(x) x = downscale(dims*2)(x) x = downscale(dims*4)(x) x = downscale(dims*8)(x) x = Dense(ae_dims)(Flatten()(x)) x = Dense(lowest_dense_res * lowest_dense_res * ae_dims)(x) x = Reshape((lowest_dense_res, lowest_dense_res, ae_dims))(x) x = upscale(ae_dims)(x) return x return func @staticmethod def DFDecFlow(output_nc, ch_dims, multiscale_count=1, add_residual_blocks=False, **kwargs): exec (nnlib.import_all(), locals(), globals()) upscale = partial(SAEModel.upscale, **kwargs) to_bgr = partial(SAEModel.to_bgr, **kwargs) dims = output_nc * ch_dims ResidualBlock = partial(SAEModel.ResidualBlock, **kwargs) def func(input): x = input[0] outputs = [] x1 = upscale(dims*8)( x ) if add_residual_blocks: x1 = ResidualBlock( dims*8 )(x1) x1 = ResidualBlock( dims*8 )(x1) if multiscale_count >= 3: outputs += [ to_bgr(output_nc) ( x1 ) ] x2 = upscale(dims*4)( x1 ) if add_residual_blocks: x2 = ResidualBlock( dims*4)(x2) x2 = ResidualBlock( dims*4)(x2) if multiscale_count >= 2: outputs += [ to_bgr(output_nc) ( x2 ) ] x3 = upscale(dims*2)( x2 ) if add_residual_blocks: x3 = ResidualBlock( dims*2)(x3) x3 = ResidualBlock( dims*2)(x3) outputs += [ to_bgr(output_nc) ( x3 ) ] return outputs return func Model = SAEModel